ABSTRACT
Confirmatory factor analytic tests of measurement invariance (MI) based on the chi-square statistic are known to be highly sensitive to sample size. For this reason, G. W. Cheung and R. B. Rensvold (2002) recommended using alternative fit indices (AFIs) in MI investigations. In this article, the authors investigated the performance of AFIs with simulated data known to not be invariant. The results indicate that AFIs are much less sensitive to sample size and are more sensitive to a lack of invariance than chi-square-based tests of MI. The authors suggest reporting differences in comparative fit index (CFI) and R. P. McDonald's (1989) noncentrality index (NCI) to evaluate whether MI exists. Although a general value of change in CFI (.002) seemed to perform well in the analyses, condition specific change in McDonald's NCI values exhibited better performance than a single change in McDonald's NCI value. Tables of these values are provided as are recommendations for best practices in MI testing.
